In the field of earth sciences, seismic sounding is used for exploring the subterranean geology of an earth formation. The acquisition of seismic data is typically accomplished by transmitting an acoustic signal into the earth and recording reflections of the signal. The layers of rock within the earth differ in their acoustic properties and these changes in properties produce different reflections of the seismic signal. Data acquisition therefore, involves energy sources generating signals propagating into the earth and reflecting from subsurface geological structures. The reflected signals are recorded by receivers on or near the surface of the earth. The reflected signals are stored as time series (pre-stack seismic traces) that consist of amplitudes of acoustic energy, which vary as a function of time, receiver position and source position. Because subsurface geological structures are different, depending on formation layers within the earth, the variation in the amplitudes of the reflected acoustic signals are indicative of the physical properties of these structures from which the signals reflect. A similar technique can be used for offshore oil and gas exploration. In either application, subsurface sedimentary structures that trap oil, such as faults and domes, are mapped by the reflective signals.
The seismic data are generally processed to create acoustic images from which data interpreters may create images of the subsurface formations. Data processing therefore, involves procedures that vary depending on the nature of the seismic data acquired and the geological structure being interpreted. A single echo (reflection) train is usually called a trace. A trace generally represents a combination of many sinusoidal waves as a function of time. The strength of the recorded reflections rises and falls over a period of several seconds, and is recorded in digital form or converted to digital form for processing and analysis. The variations in the trace generally consist of amplitude characteristics such as peaks, zero crossings and troughs.
The use of seismic data to detect subsurface change in formations by means of surface or other remote geophysical measurements made over a period of time has a long history of scientific study and commercial application. Commercial applications include, for example, monitoring i) pollutant discharge; ii) aquifer levels and breaches; iii) the effectiveness of enhanced oil recovery methods; and iv) sequestration of greenhouse gases such as carbon dioxide. In the field of petroleum exploration and production, seismic data acquired for such monitoring are often termed “time-lapse” or “4-D” seismic-data sets or surveys and may be used to monitor subsurface changes, such as expansion, contraction and fracturing, in a formation producing hydrocarbons that are induced by the extraction/injection of fluids and gases. The analysis of such changes is pertinent to determining, enhancing, and monitoring production efficiency over time.
In practice, however, the detection and analysis of changes between time-lapse seismic-data sets is impaired because it may be difficult or impossible to recreate the precise instrument positioning, earth coupling, source signature and receiver characteristics from one geophysical survey to the next. A truly repeated survey should have identical subsurface responses except where affected by actual subsurface changes. There is therefore a major commercial emphasis on improving the repeatability of field acquisition. While such approaches can indeed be effective, they are costly in time and financial resources to implement. Furthermore, extensive reprocessing of the time-lapse seismic surveys is very often needed in order to make them look as much alike as possible, leveraging the assumption that only a small portion of the subsurface will change over time. Generally, traces in each survey must be interpolated to precisely align them on a common grid and various wavelet adjustments and data warping are applied to make the data sets match.
In addition to their cost and complexity, these conventional approaches to 4-D seismic survey acquisition and analysis do not handle passive seismic monitoring wherein sources are not used and receivers are simply positioned above or around the reservoir and continuously record naturally occurring seismic energy. Such passive data presents two major obstacles to analysis: i) the energy source is inherently nonrepeatable; and ii) the seismic energy reflected from the reservoir during the continuous recording does not arrive separated either in time or space from energy reflected from strata above or below the reservoir. The latter issue is addressed in the field of seismic interferometry with published techniques such as those described by Artman, 2006, Time domain passive seismic processing at Valhall (Stanford Exploration Project Report SEP-125, p. 1-18); by Sneider, Wapenaar and Weler, 2007, Unified Green's function retrieval by cross-correlation; connection with energy principles (Phys. Rev. E, 75, 036103); and by Curtis, et al., 2006, Seismic interferometry—turning noise into signal (The Leading Edge, 25, 1082-1092) wherein passive seismic data are transformed into active source seismograms, albeit with little or no control on the signatures of these effective sources.
While development of 4-D seismic technology and techniques has been ongoing for a couple of decades, it is clearly desirable to devise a novel approach that i) relaxes, rather than tightens, the field acquisition and data processing requirements of current 4-D methodologies; and ii) can be applied to passive seismic monitoring as well as time-lapse active source seismic surveys.
To this end, two concepts may be exploited. First, the distribution of time-lapse changes in and around a reservoir are spatially coherent, i.e. changes in the strata have a real extent. As a consequence, if there are one or more measures of the reservoir interval that are both sensitive to the contents of the reservoir and have a consistent calibration across multiple time-lapse surveys, then they can be a really mapped, i.e. spatially interpolated to a uniform grid for each survey, independently, and the gridded measure(s) can then be compared. As computerized mapping is very quick and easy with present-day commercial software packages, the time and cost of generating such maps is far less than that of conventional high precision field acquisition and data processing of 4-D seismic surveys.
The second concept is that time-lapse changes in a reservoir could be measured by comparing relative changes within each trace rather than absolute differences between each trace in the surveys. Conventional state-of-the-art methodology strives to generate seismic traces that look exactly alike over regions without subsurface change so that trace-by-trace subtraction (Trace(T=2,{right arrow over (X)})−Trace(T==1,{right arrow over (X)})) will produce nonzero differences in those zones that have changed. Even small variations in source signature, receiver instrument response and shallow near-surface effects between the traces being subtracted may render the result uninterpretable. One measure of relative change within a trace is described by M. E. Willis in Spatial orientation and distribution of reservoir fractures from scattered seismic energy (Geophysics, 71(5), O43-O51) (hereinafter Willis), which is incorporated herein by reference. In Willis, traces recorded at different azimuths around a common location are analyzed by selecting a window of seismic samples above a suspected fracture zone and another window below that zone, computing auto-correlations of each window and then designing a transfer function to convert the upper auto-correlation to the lower auto-correlation. The transfer function essentially represents a spectral ratio between the window above the suspected fracture zone and the window below that zone. Thus wavelet effects common to the two windows, specifically source signature, receiver instrument response and shallow near-surface effects, are effectively cancelled out in the ratio. As wavelet repeatability is a central obstacle to time-lapse seismic survey matching, the transfer functions of Willis are especially suited to the challenges of time-lapse seismic monitoring.